A challenge that heterogeneous system programmers face is leveraging the performance of all the devices that integrate the system. This paper presents Sigmoid, a new load balancing algorithm that efficiently co-executes a single OpenCL data-parallel kernel on all the devices of heterogeneous systems. Sigmoid splits the workload proportionally to the capabilities of the devices, drastically reducing

An approach to adding concurrency to an existing undergraduate course should integrate driving problems used in analogies, worked examples, and assignments; amplify the subject/topics of the existing course; and ensure that the addition does not have a negative impact on (a) conceptual focus, and (b) student learning, struggle, and engagement. When concurrency is added to a course covering object-based

High Performance Computing (HPC) is a highly demanded discipline in companies and institutions. However, as students and also afterwards as professors, we observed a lack of HPC related content in the engineering degrees at our university, including Computer Science. Thus, we designed and offered the engineering students a non-mandatory course entitled “Build your own cluster employing Raspberry Pi”

With the increasing popularity of data sharing among users of a group in clouds, shared data auditing has become an important issue in the cloud auditing field. To address this issue, many shared data auditing schemes have been proposed in the literature based on either public key infrastructure (PKI) or identity-based cryptography (IBC). However, these schemes suffer from issues of complex certificate

Unstructured mesh particle-in-cell, PIC, simulations executing on the current and next generation of massively parallel systems require new methods for both the mesh and particles to achieve performance and scalability on GPUs. The traditional approach to implementing PIC simulations define data structures and algorithms in terms of particles with a full copy of the unstructured mesh on every process

In the big data era, large amounts of data are under generation and accumulation in various industries. However, users usually feel hindered by the data quality issues when extracting values from the big data. Thus, data quality issues are gaining more and more attention from data quality management analysts. Cutting-edge solutions like data ETL, data cleaning, and data quality monitoring systems have

This work aims at distilling a systematic methodology to modernize existing sequential scientific codes with a little re-designing effort, turning an old codebase into modern code, i.e., parallel and robust code. We propose a semi-automatic methodology to parallelize scientific applications designed with a purely sequential programming mindset, possibly using global variables, aliasing, random number

In this paper, the parallel machine scheduling problem with DeJong's learning effect, is addressed. The objective function to be minimized is the makespan. This problem is proofed to be NP-Hard in the strong sense. This is the challenging theoretical side of the studied problem. Furthermore, several real-life situations in manufacturing and computer science are modeled using the current problem. Several

HDFS faces several issues when it comes to handling a large number of small files. These issues are well addressed by archive systems, which combine small files into larger ones. They use index files to hold relevant information for retrieving a small file content from the big archive file. However, existing archive-based solutions require significant overheads when retrieving a file content since

This work is focused on improving multi-GPU performance of a research CFD code on structured grids. MPI and PGI 18.1 OpenACC directives are used to scale the code up to 16 NVIDIA GPUs. This work shows that using 16 P100 GPUs and 16 V100 GPUs can be up to 30× and 90× faster than 16 Xeon CPU E5-2680v4 cores for three different test cases, respectively. A series of performance issues related to the scaling

Teaching parallel and distributed computing topics in a hands-on manner is challenging, especially at introductory, undergraduate levels. Participation challenges arise due to the need to provide students with an appropriate compute platform, which is not always possible. Even if a platform is provided to students, not all relevant learning objectives can be achieved via hands-on learning on a single

The increase in the number of cores in a single chip brings better capabilities to exploit thread-level parallelism (TLP). However, since power dissipated per area rises at each new node generation, higher temperatures are achieved, speeding up the aging of hardware components, which may provoke undesired system behavior. Considering that many applications do not scale with the number of cores, their

Nowadays smartphones become an indispensable tool in many people's everyday life that makes themselves attractive targets for attackers. Among various malware targeting at smartphones, SMS-based malware is one of the most notorious ones. Though a number of Android dynamic analysis frameworks have been proposed to analyze SMS-based malware, most of these frameworks or some Android tools, such as Google

In the curriculum of a Computer Engineering program, concepts like parallelism, concurrency, consistency, or atomicity are usually addressed in separate courses due to their thoroughness and extension. Isolating such concepts in courses helps students not only to focus on specific aspects, but also to experience the reality of working with modern computer systems, where those concepts are often detached

Edge computing for Internet of Things (IoT) is a promising framework that can help small devices such as low-powered sensor nodes to accomplish complex computational tasks. The limited power supply of sensor nodes is one of their major limitations. A successful approach to improve the network lifetime and the overall scalability of the IoT supported wireless sensor networks (WSNs) is clustering. However

The Industrial Internet of Things (IIoT) enhances smart manufacturing process that escalates productivity through revolutionary techniques. The manufacturing process is sophisticated and complex because of various IoT domains (e.g. industries). A final product is an outcome of the efforts of several departments from different industries. However, this raises the cross-domain communication's privacy

There is some evidence that labeling schemes as employed for instance in the famous Coffman-Graham algorithm may provide superior worst-case approximation guarantees than purely path- or level-based list schedules in the presence of (delayed) precedence constraints. In 1989, Bernstein, Rodeh, and Gertner proved that this also holds true for their labeling scheme targeting unit execution time tasks

Very recently, a number of intelligent applications have evolved in smart cities such as smart grid, smart parking, smart waste management, smart manufacturing, smart home, and smart health, making our cities more smarter. In the context of smart health, smart devices are employed to collect and transmit biomedical information to a medical server, allowing medical practitioners to give better and improved

The unrelenting growth of data intensive applications has been raising the bar for performance of high speed networks. To cater for this growth, the core segments of networks today are based on 100 Gbps links. Analyzing the huge volume of traffic over these networks is a challenging yet essential task from the perspectives of network administration, network security and law enforcement. In spite of

System-on-Chip (SoC) based Field Programmable Gate Arrays (FPGAs) provide a hardware acceleration technology that can be rapidly deployed and tuned, thus providing a flexible solution adaptable to specific design requirements and to changing demands. In this paper, we present three SoC architecture designs for speeding-up inference tasks based on machine learned ensembles of decision trees. We focus

Teaching parallel programming and HPC is a difficult task. There is a large number of sophisticated hardware and software components, each complex on their own and often showing non-intuitive interaction when used in combination. We consider education in HPC among the more difficult topics in computer science due to the fact that larger distributed memory systems are ubiquitous yet inaccessible and

Some Deep Neural Networks (DNN) have what we call lanes, or they can be reorganized as such. Lanes are paths in the network which are data-independent and typically learn different features or add resilience to the network. Given their data-independence, lanes are amenable for parallel processing. The Multi-lane CapsNet (MLCN) is a proposed reorganization of the Capsule Network which is shown to achieve

Programming courses usually focus on software-engineering problems like software decomposition and code maintenance. While computer-science lessons emphasize algorithm complexity, technological problems are usually neglected although they may significantly affect the performance in terms of wall time. As the technological problems are best explained by hands-on experience, we present a set of homework

This paper provides a true-concurrency approach for the specification and verification of systems of concurrent communicating agents with durable actions. We present high-level Petri nets with durable actions (DaHL) to cope with various details in such complex systems. We define a DaHL module as an open variant of time-dependent colored Petri nets. A DaHL system is a fused set of modules for systems

Hash tries are a trie-based data structure with nearly ideal characteristics for the implementation of hash maps. Starting from a particular lock-free hash map data structure, named Lock-Free Hash Tries, we focus on solving the problem of memory reclamation without losing the lock-freedom property. To the best of our knowledge, outside garbage collected environments, there is no current implementation

Training on Grid technologies has traditionally used existing Grid infrastructures to implement the hands-on education activities. However, these infrastructures are insufficient to develop all training skills as they can only be employed for the development of Grid applications, and they are limited for learning the management and configuration of Grid resources. The paper presents a set of educational

Reliability, and safety are considered as the indispensable twins during the adoption of the cloud computing environment since their breaches lead to catastrophic issues in poor resource management and unreliable service quality. To be specific, Distributed Denial of Service (DDoS) attack is determined as the most vulnerable threat in the cloud space as it lowers the ability of the predominant resources’

This paper proposes an efficient approach for the parallel solution of computationally time consuming problems of multiple global optimization, in which minimized functions can be multiextremal and calculating function values may require huge amounts of computations. The proposed approach is based on the information-statistical theory of global optimization, within which a general computational scheme

Auto-scaling systems can dynamically scale the required resources for cloud-based services at runtime. This is an effective mechanism, enabling services to adapt to environmental changes. These systems establish the foundation for achieving elasticity in the modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, cloud auto-scaling systems are one

The Internet of Things (IoT) becomes a leading field of computer science, which simplifies the life of human beings. The IoT, devices can interconnect and communicate with each other, in a fully autonomous mode. This autonomy has made the IoT network vulnerable to various security threats. However, due to this autonomy, there is a strong need for reliable security and storage mechanism for authentication

Modern virtualized data centers often rely on virtual machine (VM) migrations to consolidate workload on a single machine for energy saving. But VM migrations have many drawbacks, including performance degradation, service disruption etc. Hence, many approaches have been proposed to minimize the overhead when migrations occur. In contrast, this work aims to proactively avoid migrations from happening

Kernel matrix-vector product is ubiquitous in many science and engineering applications. However, a naive method requires O(N2) operations, which becomes prohibitive for large-scale problems. To reduce the computation cost, we introduce a parallel method that provably requires O(N) operations and delivers an approximate result within a prescribed tolerance. The distinct feature of our method is that

Network congestion is one of the critical reasons for degradation of data throughput performance in Networks-on-Chip (NoCs), with delays caused by data-buffer queuing in routers. Local buffer or router congestion impacts on network performance as it gradually spreads to neighbouring routers and beyond. In this paper, we propose a novel approach to NoC traffic prediction using Spiking Neural Networks

Given a connected graph G and S⊆V(G) with |S|≥2, a tree T in G is called an S-Steiner tree (or S-tree for short) if S⊆V(T). Two S-trees T1 and T2 are internally disjoint if E(T1)∩E(T2)=∅ and V(T1)∩V(T2)=S. The packing number of internally disjoint S-trees, denoted as κG(S), is the maximum size of a set of internally disjoint S-trees in G. For an integer k≥2, the generalized k-connectivity (abbr. κk-connectivity)

The powerful resource advantage of the cloud provides a suitable computing environment for data processing. By transferring local computing to the cloud, the efficiency of data processing can be improved. However, the open cloud environment has defects in data privacy-preserving. In order to strengthen the protection of data privacy and ensure the security of multi-party interaction, we propose a privacy-preserving

High performance computing systems are required to solve grand challenges in many scientific disciplines. These systems assemble many components to be powerful enough for solving extremely complex problems. An inherent consequence is the intricacy of the interaction of all those components, especially when failures come into the picture. It is crucial to develop an understanding of how these systems

In recent years, the use of directional sensor networks (DSNs) has continued to rise increasingly, which is due to their extensive use in many situations. In such networks, the main problem is how to cover the targets distributed in a defined area and simultaneously prolong the network lifetime as much as possible. The reason of this problem is the limitation of both sensing angle and energy resource

Lightweight manycore processors deliver high performance and scalability by bundling in a single chip hundreds of low-power cores, a distributed memory architecture and Networks-on-Chip (NoCs). Operating Systems (OSes) for these processors feature a distributed design, in which a communication layer enables kernels to exchange information and interoperate. Currently, this communication infrastructure

We study the impact of communication latency on the classical Work Stealing load balancing algorithm. Our paper extends the reference model in which we introduce a latency parameter. By using a theoretical analysis and simulation, we study the overall impact of this latency on the Makespan (maximum completion time). We derive a new expression of the expected running time of a bag of independent tasks

Mutual exclusion and consensus are among the most important coordination problems encountered in asynchronous concurrent systems, whether processes communicate using read/write registers or message passing. Unfortunately, neither can be solved in crash-prone systems, as soon as even a single process may crash. Hence, an important question: which is the weakest information on failures that must be given

The problem of coverage in two-dimensional (2D) wireless sensor networks is challenging and is still open. Precisely, determining the minimum sensor density (i.e, minimum number of sensors per unit area) that is required to cover a 2D field of interest (FoI), where every point in the field is covered by at least one sensor, is still under investigation. The problem of 2D k-coverage, which requires

We study the problem of task reallocation for load-balancing of MapReduce jobs in applications that process large datasets. In this context, we propose a novel strategy based on cooperative agents used to optimize the task scheduling in a single MapReduce job. The novelty of our strategy lies in the ability of agents to identify opportunities within a current unbalanced allocation, which in turn triggers

Parallel and distributed computing (PDC) education is increasingly gaining greater recognition as a core topic in undergraduate computing degrees. While the application of PDC concepts to software development involves the use of highly-technical tools and libraries typically reserved for advanced courses, PDC educators are seeking pedagogical approaches that can be used to introduce PDC concepts in

Proactive fault management is an important problem in many areas of data management, including cloud computing, big data, vision, machine learning and especially for the cross-domain research of distributed computing and AI (Artificial Intelligence). Unfortunately, most real-world online failure prediction is facing the problem that the used data are difficult to label although the failure prediction

The bi-directional flow of energy and information in the smart grid makes it possible to record and analyze the electricity consumption profiles of consumers. Because of the increasing rate of inflation over the past few years, people started looking for means to use electricity illegally, termed as electricity theft. Many data analytics techniques are proposed in the literature for electricity theft

The deployment of edge computing infrastructure requires a careful placement of the edge servers, with an aim to improve application latencies and reduce data transfer load in opportunistic Internet of Things systems. In the edge server placement, it is important to consider computing capacity, available deployment budget, and hardware requirements for the edge servers and the underlying backbone network

Today’s High Performance Computing (HPC) systems contain thousand of nodes which work together to provide performance in the order of petaflops. The performance of these systems depends on various components like processors, memory, and interconnect. Among all, interconnect plays a major role as it glues together all the hardware components in an HPC system. A slow interconnect can impact a scientific

Hybrid cloud is a cost-effective way to address the problem of insufficient resources for satisfying its users’ requirements in a private cloud by elastically scaling up or down its service capability by combining the private cloud and public clouds. However, it is a challenge to schedule tasks on hybrid resources concerning their performance and security requirements. To address the challenge, this

Modern GPPs implement specialized instructions in the form of ISA extensions aiming to increase the performance of emerging applications. These extensions impose a significant overhead in the area and power of the processor because of their specific datapaths (e.g. hardware for SIMD and FP instructions may represent more than half of the core area). Considering that some devices (e.g., edge computing)

Cyber-physical system (CPS) is the incorporation of physical processes with processing and data transmission. Cybersecurity is a primary and challenging issue in healthcare due to the legal and ethical perspective of the patient’s medical data. Therefore, the design of CPS model for healthcare applications requires special attention for ensuring data security. To resolve this issue, this paper proposes

In edge computing, smart contracts usually run in the form of containers on edge nodes. However, the container has process nesting and strong association with image files, which leads to insufficient real-time migration. This article studies the container-based service migration problem in edge computing. In order to reduce the service delay of nodes, we propose a service migration mechanism based

Industrial data plays a key role in the industrial internet, and its secure collection problem has been highly valued by researchers. As Industrial Internet of Things (IIoT) devices are geographically dispersed and difficult to link, blockchain technology is usually introduced to solve the security management problem of industrial data. Unfortunately, the IIoT device is not stable, and it may leave

Critical event information dissemination has been proliferating on VANET allowing road safety via connected vehicular communications. Despite the prospect of promising applications in vehicular networks, it faces unresolved challenges that hold the capability to slow down network performance upon deployment, especially in terms of security. Particularly, insider attacks such as Blackhole attacks that

The traditional method to study application resilience to errors in HPC applications uses fault injection (FI), a time-consuming approach. While analytical models have been built to overcome the inefficiencies of FI, they lack accuracy. In this paper, we present PARIS, a machine-learning method to predict application resilience that avoids the time-consuming process of random FI and provides higher

Blockchain is a relative recent research and technological trend, with applications in diverse domains including those associated with a nation’s critical infrastructure sectors (e.g., chemical, commercial facilities, communications, critical manufacturing, dams, defense industrial base, emergency services, and energy). The interest in blockchain is also partly evidenced by the number of submissions

Copyright infringement adversely affects the interest of copyright holders of images and videos which are uploaded to different websites and peer-to-peer image sharing systems. This paper addresses the problem of detecting copyright infringement so that copyright holders are given due credit for their work. There are several images and videos that are shared every day by millions of users with some

The paradigm of cloud computing has heralded a new avenue of computing, offering benefits of increased data accessibility with low cost. Continuous Writing Applications (CWA) (e.g., augmented online services for Health Care) have specific requirements on data storage, computation and bandwidth, thus are cost-sensitive with limited budgets and time. Herein, we propose an architecture of multi-cloud

We are thrilled and delighted to present this special issue, which emphasizes on the novel area of Enabling Technologies for Energy Cloud. This guest editorial provides an overview of all articles accepted for publication in this special issue.

The problem of using cloud computing resources for services is related to planning the amount of resources needed and their subsequent reservation. This problem occurs both on the side of the customer who tries to minimize the cost of the service and on the side of the cloud provider who wants to make the best use of existing infrastructure without introducing any modifications. In our article, we